A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.
Its goal is to become the AUTOMATIC1111/stable-diffusion-webui of text generation.
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- Dropdown menu for switching between models
- Notebook mode that resembles OpenAI's playground
- Chat mode for conversation and role-playing
- Instruct mode compatible with various formats, including Alpaca, Vicuna, Open Assistant, Dolly, Koala, ChatGLM, MOSS, RWKV-Raven, Galactica, StableLM, WizardLM, Baize, Ziya, Chinese-Vicuna, MPT, INCITE, Wizard Mega, KoAlpaca, Vigogne, Bactrian, h2o, and OpenBuddy
- Multimodal pipelines, including LLaVA and MiniGPT-4
- Markdown output with LaTeX rendering, to use for instance with GALACTICA
- Nice HTML output for GPT-4chan
- Custom chat characters
- Advanced chat features (send images, get audio responses with TTS)
- Very efficient text streaming
- Parameter presets
- LLaMA model
- 4-bit GPTQ mode
- LoRA (loading and training)
- llama.cpp
- RWKV model
- 8-bit and 4-bit through bitsandbytes
- Layers splitting across GPU(s), CPU, and disk
- CPU mode
- FlexGen
- DeepSpeed ZeRO-3
- API with streaming and without streaming
- Extensions - see the user extensions list
| Windows | Linux | macOS |
|---|---|---|
| oobabooga-windows.zip | oobabooga-linux.zip | oobabooga-macos.zip |
Just download the zip above, extract it, and double-click on "start". The web UI and all its dependencies will be installed in the same folder.
- The source codes are here: https://github.com/oobabooga/one-click-installers
- There is no need to run the installers as admin.
- AMD doesn't work on Windows.
- Huge thanks to @jllllll, @ClayShoaf, and @xNul for their contributions to these installers.
Recommended if you have some experience with the command line.
On Windows, I additionally recommend carrying out the installation on WSL instead of the base system: WSL installation guide.
https://docs.conda.io/en/latest/miniconda.html
On Linux or WSL, it can be automatically installed with these two commands:
curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh"
bash Miniconda3.sh
Source: https://educe-ubc.github.io/conda.html
conda create -n textgen python=3.10.9
conda activate textgen
| System | GPU | Command |
|---|---|---|
| Linux/WSL | NVIDIA | pip3 install torch torchvision torchaudio |
| Linux | AMD | pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2 |
| MacOS + MPS (untested) | Any | pip3 install torch torchvision torchaudio |
The up-to-date commands can be found here: https://pytorch.org/get-started/locally/.
- MacOS users: oobabooga#393
- AMD users: https://rentry.org/eq3hg
git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r requirements.txt
The base installation covers transformers models (AutoModelForCausalLM and AutoModelForSeq2SeqLM specifically) and llama.cpp (GGML) models.
To use GPTQ models, the additional installation steps below are necessary:
bitsandbytes >= 0.39 may not work on older NVIDIA GPUs. In that case, to use --load-in-8bit, you may have to downgrade like this:
- Linux:
pip install bitsandbytes==0.38.1 - Windows:
pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl
As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: Windows installation guide.
ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} .
cp docker/.env.example .env
# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model
docker compose up --build
- You need to have docker compose v2.17 or higher installed. See this guide for instructions.
- For additional docker files, check out this repository.
From time to time, the requirements.txt changes. To update, use this command:
conda activate textgen
cd text-generation-webui
pip install -r requirements.txt --upgrade
Models should be placed inside the models/ folder.
Hugging Face is the main place to download models. These are some examples:
You can automatically download a model from HF using the script download-model.py:
python download-model.py organization/model
For example:
python download-model.py facebook/opt-1.3b
If you want to download a model manually, note that all you need are the json, txt, and pytorch*.bin (or model*.safetensors) files. The remaining files are not necessary.
You can drop these directly into the models/ folder, making sure that the file name contains ggml somewhere and ends in .bin.
GPT-4chan has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:
The 32-bit version is only relevant if you intend to run the model in CPU mode. Otherwise, you should use the 16-bit version.
After downloading the model, follow these steps:
- Place the files under
models/gpt4chan_model_float16ormodels/gpt4chan_model. - Place GPT-J 6B's config.json file in that same folder: config.json.
- Download GPT-J 6B's tokenizer files (they will be automatically detected when you attempt to load GPT-4chan):
python download-model.py EleutherAI/gpt-j-6B --text-only
conda activate textgen
cd text-generation-webui
python server.py
Then browse to
http://localhost:7860/?__theme=dark
Optionally, you can use the following command-line flags:
| Flag | Description |
|---|---|
-h, --help |
Show this help message and exit. |
--notebook |
Launch the web UI in notebook mode, where the output is written to the same text box as the input. |
--chat |
Launch the web UI in chat mode. |
--character CHARACTER |
The name of the character to load in chat mode by default. |
--model MODEL |
Name of the model to load by default. |
--lora LORA [LORA ...] |
The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. |
--model-dir MODEL_DIR |
Path to directory with all the models. |
--lora-dir LORA_DIR |
Path to directory with all the loras. |
--model-menu |
Show a model menu in the terminal when the web UI is first launched. |
--no-stream |
Don't stream the text output in real time. |
--settings SETTINGS_FILE |
Load the default interface settings from this yaml file. See settings-template.yaml for an example. If you create a file called settings.yaml, this file will be loaded by default without the need to use the --settings flag. |
--extensions EXTENSIONS [EXTENSIONS ...] |
The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
--verbose |
Print the prompts to the terminal. |
| Flag | Description |
|---|---|
--cpu |
Use the CPU to generate text. Warning: Training on CPU is extremely slow. |
--auto-devices |
Automatically split the model across the available GPU(s) and CPU. |
--gpu-memory GPU_MEMORY [GPU_MEMORY ...] |
Maxmimum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB. |
--cpu-memory CPU_MEMORY |
Maximum CPU memory in GiB to allocate for offloaded weights. Same as above. |
--disk |
If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
--disk-cache-dir DISK_CACHE_DIR |
Directory to save the disk cache to. Defaults to cache/. |
--load-in-8bit |
Load the model with 8-bit precision (using bitsandbytes). |
--bf16 |
Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
--no-cache |
Set use_cache to False while generating text. This reduces the VRAM usage a bit with a performance cost. |
--xformers |
Use xformer's memory efficient attention. This should increase your tokens/s. |
--sdp-attention |
Use torch 2.0's sdp attention. |
--trust-remote-code |
Set trust_remote_code=True while loading a model. Necessary for ChatGLM and Falcon. |
| Flag | Description |
|---|---|
--load-in-4bit |
Load the model with 4-bit precision (using bitsandbytes). |
--compute_dtype COMPUTE_DTYPE |
compute dtype for 4-bit. Valid options: bfloat16, float16, float32. |
--quant_type QUANT_TYPE |
quant_type for 4-bit. Valid options: nf4, fp4. |
--use_double_quant |
use_double_quant for 4-bit. |
| Flag | Description |
|---|---|
--threads |
Number of threads to use. |
--n_batch |
Maximum number of prompt tokens to batch together when calling llama_eval. |
--no-mmap |
Prevent mmap from being used. |
--mlock |
Force the system to keep the model in RAM. |
--cache-capacity CACHE_CAPACITY |
Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
--n-gpu-layers N_GPU_LAYERS |
Number of layers to offload to the GPU. Only works if llama-cpp-python was compiled with BLAS. Set this to 1000000000 to offload all layers to the GPU. |
--n_ctx N_CTX |
Size of the prompt context. |
--llama_cpp_seed SEED |
Seed for llama-cpp models. Default 0 (random). |
| Flag | Description |
|---|---|
--wbits WBITS |
Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
--model_type MODEL_TYPE |
Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. |
--groupsize GROUPSIZE |
Group size. |
--pre_layer PRE_LAYER [PRE_LAYER ...] |
The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60. |
--checkpoint CHECKPOINT |
The path to the quantized checkpoint file. If not specified, it will be automatically detected. |
--monkey-patch |
Apply the monkey patch for using LoRAs with quantized models. |
--quant_attn |
(triton) Enable quant attention. |
--warmup_autotune |
(triton) Enable warmup autotune. |
--fused_mlp |
(triton) Enable fused mlp. |
| Flag | Description |
|---|---|
--autogptq |
Use AutoGPTQ for loading quantized models instead of the internal GPTQ loader. |
--triton |
Use triton. |
| Flag | Description |
|---|---|
--flexgen |
Enable the use of FlexGen offloading. |
--percent PERCENT [PERCENT ...] |
FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0). |
--compress-weight |
FlexGen: Whether to compress weight (default: False). |
--pin-weight [PIN_WEIGHT] |
FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%). |
| Flag | Description |
|---|---|
--deepspeed |
Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. |
--nvme-offload-dir NVME_OFFLOAD_DIR |
DeepSpeed: Directory to use for ZeRO-3 NVME offloading. |
--local_rank LOCAL_RANK |
DeepSpeed: Optional argument for distributed setups. |
| Flag | Description |
|---|---|
--rwkv-strategy RWKV_STRATEGY |
RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
--rwkv-cuda-on |
RWKV: Compile the CUDA kernel for better performance. |
| Flag | Description |
|---|---|
--listen |
Make the web UI reachable from your local network. |
--listen-host LISTEN_HOST |
The hostname that the server will use. |
--listen-port LISTEN_PORT |
The listening port that the server will use. |
--share |
Create a public URL. This is useful for running the web UI on Google Colab or similar. |
--auto-launch |
Open the web UI in the default browser upon launch. |
--gradio-auth USER:PWD |
set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3" |
--gradio-auth-path GRADIO_AUTH_PATH |
Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3" |
| Flag | Description |
|---|---|
--api |
Enable the API extension. |
--public-api |
Create a public URL for the API using Cloudfare. |
--api-blocking-port BLOCKING_PORT |
The listening port for the blocking API. |
--api-streaming-port STREAMING_PORT |
The listening port for the streaming API. |
| Flag | Description |
|---|---|
--multimodal-pipeline PIPELINE |
The multimodal pipeline to use. Examples: llava-7b, llava-13b. |
Out of memory errors? Check the low VRAM guide.
Inference settings presets can be created under presets/ as text files. These files are detected automatically at startup.
By default, 10 presets based on NovelAI and KoboldAI presets are included. These were selected out of a sample of 43 presets after applying a K-Means clustering algorithm and selecting the elements closest to the average of each cluster.
Make sure to check out the documentation for an in-depth guide on how to use the web UI.
https://github.com/oobabooga/text-generation-webui/tree/main/docs
Pull requests, suggestions, and issue reports are welcome.
You are also welcome to review open pull requests.
Before reporting a bug, make sure that you have:
- Created a conda environment and installed the dependencies exactly as in the Installation section above.
- Searched to see if an issue already exists for the issue you encountered.
- Gradio dropdown menu refresh button, code for reloading the interface: https://github.com/AUTOMATIC1111/stable-diffusion-webui
- NovelAI and KoboldAI presets: https://github.com/KoboldAI/KoboldAI-Client/wiki/Settings-Presets
- Code for early stopping in chat mode, code for some of the sliders: https://github.com/PygmalionAI/gradio-ui/